Introduction Non-Negative Matrix Factorization (NMF) is a supervised algorithm used to represent data into lower dimensions which reduces the number of features while preserving enough basic information to construct the original matrix from the reduced feature space. In this article, we will be going explore more about NMF and how it can be useful. Non-Negative Matrix Factorization NMF is used to reduce the dimensions of the input matrix or corpus. It uses factor analysis which gives less importance to less relevant words. The decomposition of the original matrix(which is a non-negative matrix) thus creates a product of two non-negative coefficients ... Read More

Introduction Multilingual Google Meet summarizer is a tool/chrome extension that can create transcriptions for google meet conversations in multiple languages. During the COVID times people, they need a tool that can effectively summarize meetings, classroom lectures, and convection videos. Thus such a tool can be quite useful in this regard. In this article, let us have an overview of the project structure and explore some implementation aspects with the help of code. What this project is all about? This is a simple chrome extension that when enabled during a google meet session can generate meeting transcriptions and summarize the conversation ... Read More

Introduction Teaching Learning Based Optimization (TLBO) is based on the relationship between a teacher and the learners in a class. In a particular class, a teacher imparts knowledge to the students through his/her hard work. The students or learners then interact with each other among themselves and improve their knowledge. Let us explore more about Teacher Learning Based Optimization through this article. What is TLBO? Let us consider a population p (particularly a class) and the number of learners l in the class. There may be decisive variables (subjects from which learners gain knowledge) for the optimization problem. Two modes ... Read More

Introduction The Particle Swarm Optimization algorithm is inspired by nature and is based on the social behavior of birds in a flock or the behavior of fish and is a population-based algorithm for search. It is a simulation to discover the pattern in which birds fly and their formations and grouping during flying activity. Particle Swarm Optimization Algorithm In the PSO Algorithm, each individual is considered to be a particle in some high-dimensional search space. Inspired by the social and psychological behavior of people, which they tend to copy from other people's success, similar changes are made to the particles ... Read More

Introduction Image Segmentation is the process of partitioning an image into multiple regions based on the characteristics of the pixels in the original image. Clustering is a technique to group similar entities and label them. Thus, for image segmentation using clustering, we can cluster similar pixels using a clustering algorithm and group a particular cluster pixel as a single segment. Thus, let's explore more image segmentation using clustering, Image Segmentation The process of image segmentation by clustering can be carried out using two methods. Agglomerative clustering Divisive clustering In Agglomerative clustering, we label a pixel to a close ... Read More

Introduction RPA stands for Robotic Process Automation. An RPA developer is a person who designs, maintains, builds, and implements RPA systems. In an organization, an RPA developer has the role to create optimized workflow processes and work cross-functionally with operations and business analysts. Scope of an RPA Developer Today the world is moving towards automation where organizations maximum repetitive processes to be automated as much as possible. Thus the demand for highly skilled RPA professionals has gained pace. With the right skill set, RPA developers can fill the in the respective domains. An RPA developer is essentially a software developer ... Read More

Introduction Exploratory Data Analysis (EDA) is the process of summarization of a dataset by analyzing it. It is used to investigate a dataset and lay down its characteristics. EDA is a fundamental process in many Data Science or Analysis tasks. Different types of Exploratory Data Analysis There are broadly two categories of EDA Univariate Exploratory Data Analysis – In Univariate Data Analysis we use one variable or feature to determine the characteristics of the dataset. We derive the relationships and distribution of data concerning only one feature or variable. In this category, we have the liberty to use either ... Read More

Introduction Document Retrieval in Machine Learning is part of a larger aspect known as Information Retrieval, where a given query by the user, the system tries to find relevant documents to the search query as well as rank them in order of relevance or match. They are different ways of Document retrieval, two popular ones are − Boolean Model Vector Space Model Let us have a brief understanding of each of the above methods. Boolean Model It is a set-based retrieval model.The user query is in boolean form. Queries are joined using AND, OR, NOT, etc. A document ... Read More

Introduction The graph is a very useful data structure that can represent co-interactions. These co-interactions can be encoded by neural networks as embeddings to be used in different ML Algorithms. This is where the DeepWalk algorithm shines. In this article, we are going to explore the DeepWalk algorithm with a Word2Vec example. Let us learn more about Graph Networks on which the core of this algorithm is based. The Graph If we consider a particular ecosystem, a graph generally represents the interaction between two or more entities. A Graph Network has two objects – node or vertex and edge. ... Read More

Deep Learning provides a new horizon to the Internet of Things. The availability of different IoT sensors helps us collect data that is so important for Deep Learning. There are many uses of Deep Learning in IoT, which makes IoT more powerful. Deep Learning and the Internet of Things together form a new era that is more advanced than Web 3.0. IoT Divisions The major key sub-divisions under IoT, like IoP (Internet of People), IoT (Internet of Everything), and IIoT (Industrial Internet of Things), are developing and highly dependent upon Deep Learning technology. There are many different Deep Learning Models ... Read More